A novel hole patching algorithm for discrete geometry using non‐uniform rational B‐spline
Bibliographic record
Abstract
Abstract A hole patching algorithm for discrete geometry using a parametric approach based on Non‐Uniform Rational B‐Spline (NURBS) curves and surfaces is being presented in this paper. This algorithm is focused on recovering the missing geometric information based on the neighboring points surrounding the topologically simple but geometrically complex holes. The neighboring points are utilized to reconstruct a set of three‐dimensional (3D) NURBS surface patches covering the hole region which in turn are used to produce a smooth surface patch covering the hole. This algorithm can automatically identify the hole, obtain surrounding points, create the NURBS surfaces, and perform the projection of points onto the surface to complete the process. The algorithm also provides a way for the user to control the size and density of triangulation in the patches matching that of the surroundings. This paper describes the algorithm step by step in detail with a test case for description purposes. The validation of the algorithm is performed on several analytical geometries to assess the accuracy of the algorithm. Several complex geometries are used to demonstrate the success and robustness of the algorithm. Finally, a conclusion provides the overall assessment of this research and identifies its weaknesses for future improvements. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".